The field of radiogenomics largely focuses on developing imaging surrogates for genomic signatures and integrating imaging, genomic, and molecular data to develop combined personalized biomarkers for characterizing various diseases. Our study aims to highlight the current state-of-the-art and the role of radiogenomics in cancer research, focusing mainly on solid tumors, and is broadly divided into four sections. The first section reviews representative studies that establish the biologic basis of radiomic signatures using gene expression and molecular profiling information. The second section includes studies that aim to non-invasively predict molecular subtypes of tumors using radiomic signatures. The third section reviews studies that evaluate the potential to augment the performance of established prognostic signatures by combining complementary information encoded by radiomic and genomic signatures derived from cancer tumors. The fourth section includes studies that focus on ascertaining the biological significance of radiomic phenotypes. We conclude by discussing current challenges and opportunities in the field, such as the importance of coordination between imaging device manufacturers, regulatory organizations, health care providers, pharmaceutical companies, academic institutions, and physicians for the effective standardization of the results from radiogenomic signatures and for the potential use of these findings to improve precision care for cancer patients.
Ductal in-situ carcinoma (DCIS) is a non-invasive proliferation that lacks the ability to metastasize. Over the past four decades, DCIS diagnoses have increased ten-fold, with treatments nearly as aggressive as those for small low-grade invasive breast cancer. In this study, we evaluate the potential of identifying intrinsic imaging phenotype of DCIS using radiomic signatures from breast DCE-MRI. The rationale is that such phenotypes may capture aspects of the heterogeneity of DCIS that can aid in identifying indolent from aggressive disease to better stratify patients for improved disease management. An initial analysis was performed on eighty- two DCIS cases from the ECOG-ACRIN E4112 trial. The Cancer Phenomics Toolkit (CapTK) was used to extract a total of 95 3-D radiomic features from each primary lesion volume in pre-treatment, pre-operative breast DCE-MRI images. Features were first filtered for robustness across the heterogeneous clinical sites of DCE-MRI acquisition and features deemed non-robust (59) were discarded. Dimensionality reduction was performed with the remaining thirty-six features via principle component analysis (PCA). Unsupervised hierarchical clustering of the resulting five principal components (PCs) capturing 85% of the original feature variance was applied. Two significant intrinsic DCIS radiomic phenotypes were identified (p<0.001). Our hypothesis is that DCIS imaging biomarkers could improve prognostic ability more reliably than biopsy alone. These findings will be further explored in the expanded analysis of ECOG-ACRIN E4112 trial.
Background: Lung cancer is one of the most common cancers in the United States and the most fatal, with 142,670 deaths in 2019. Accurately determining tumor response is critical to clinical treatment decisions, ultimately impacting patient survival. To better differentiate between non-small cell lung cancer (NSCLC) responders and non-responders to therapy, radiomic analysis is emerging as a promising approach to identify associated imaging features undetectable by the human eye. However, the plethora of variables extracted from an image may actually undermine the performance of computer-aided prognostic assessment, known as the curse of dimensionality. In the present study, we show that correlative-driven hierarchical clustering improves high-dimensional radiomics-based feature selection and dimensionality reduction, ultimately predicting overall survival in NSCLC patients. Methods: To select features for high-dimensional radiomics data, a correlation-incorporated hierarchical clustering algorithm automatically categorizes features into several groups. The truncation distance in the resulting dendrogram graph is used to control the categorization of the features, initiating low-rank dimensionality reduction in each cluster, and providing descriptive features for Cox proportional hazards (CPH)-based survival analysis. Using a publicly available non- NSCLC radiogenomic dataset of 204 patients’ CT images, 429 established radiomics features were extracted. Low-rank dimensionality reduction via principal component analysis (PCA) was employed (𝒌 = 𝟏, 𝒏 < 𝟏) to find the representative components of each cluster of features and calculate cluster robustness using the relative weighted consistency metric. Results: Hierarchical clustering categorized radiomic features into several groups without primary initialization of cluster numbers using the correlation distance metric (as a function) to truncate the resulting dendrogram into different distances. The dimensionality was reduced from 429 to 67 features (for truncation distance of 0.1). The robustness within the features in clusters was varied from -1.12 to -30.02 for truncation distances of 0.1 to 1.8, respectively, which indicated that the robustness decreases with increasing truncation distance when smaller number of feature classes (i.e., clusters) are selected. The best multivariate CPH survival model had a C-statistic of 0.71 for truncation distance of 0.1, outperforming conventional PCA approaches by 0.04, even when the same number of principal components was considered for feature dimensionality. Conclusions: Correlative hierarchical clustering algorithm truncation distance is directly associated with robustness of the clusters of features selected and can effectively reduce feature dimensionality while improving outcome prediction.
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